Modular neural networks prediction model based A2/O process control system

International Journal of Precision Engineering and Manufacturing

Volumn 13, Issue 6, 2012, Pages 905-913

  Baek, Gyeongdong ^a   Cheon, Seong Pyo ^a   Kim, Sudae ^a   Kim, Yejin ^a   Kim, Hyosoo ^a   Kim, Changwon ^a   Kim, Sungshin ^a  

^a Pusan National University   (South Korea)

Author keywords

Dissolved oxygen concentration prediction; Model selection; Modular neural networks

Indexed keywords

BIOLOGICAL WATER TREATMENT; DISSOLVED OXYGEN; EFFLUENTS; FORECASTING; MICROORGANISMS; PREDICTIVE ANALYTICS; PROCESS CONTROL; WASTEWATER TREATMENT; WATER QUALITY;

BIOLOGICAL WASTEWATER TREATMENT PROCESS; DISSOLVED OXYGEN CONCENTRATIONS; LABORATORY ENVIRONMENT; MODEL SELECTION; MODULAR NEURAL NETWORKS; PREDICTION METHODOLOGY; STEADY-STATE BEHAVIORS; TIME-DEPENDENT VARIABLES;

NEURAL NETWORKS;

EID: 84861899629     PISSN: 12298557     EISSN: None     Source Type: Journal    
DOI: 10.1007/s12541-012-0118-z     Document Type: Article

References (30)

1. Comeau, Y., Hall, K. J., Hancock, R. E. W., and Oldham, W. K., "Biochemical model for enhanced biological phosphorus removal," Water Research, Vol. 20, No. 12, pp. 1511-1521, 1986.
2. Mino, T., van Loosdrecht, M. C. M., and Heijnen, J. J., "Microbiology and biochemistry of the enhanced biological phosphate removal process," Water Research, Vol. 32, No. 11, pp. 3193-3207, 1998.
3. Wang, W., Wang, S., Peng, Y., Zhang, S., and Yin, F., "Enhanced biological nutrients removal in modified step-feed anaerobic/anoxic/oxic process," Chinese Journal of Chemical Engineering, Vol. 17, No. 5, pp. 840-848, 2009.
4. Tchobanoglous, G., Burton, F. L., and Stensel, H. D., Waste water Engineering: Treatment and Reuse, 4th ed., McGraw-Hill, 2003.
5. Dellana, S. A. and West, D., "Predictive modeling for wastewater applications: Linear and nonlinear approaches," Environmental Modeling and Software, Vol. 24, No. 1, pp. 96-106, 2009.
6. Jain, A. and Kumar, A., "An evaluation of artificial neural network technique for the determination of infiltration model parameters," Applied Soft Computing, Vol. 6, No. 3, pp. 272-282, 2006.
7. Mjalli, F. S., Al-Asheh, S., and Alfadala, H. E., "Use of artificial neural network black-box modeling for the prediction of wastewater treatment plants performance," Journal of Environment Management, Vol. 83, No. 3, pp. 329-338, 2007.
8. Alvisi, S. and Franchini, M., "Fuzzy neural networks for water level and discharge forecasting with uncertainty," Environmental Modeling & Software, Vol. 26, No. 4, pp. 523-537, 2011.
9. Wong, W. K., Xia, M., and Chu, W. C., "Adaptive neural network model for time-series forecasting," European Journal of Operational Research, Vol. 207, No. 2, pp. 807-816, 2010.
10. Zhang, G. P., "Time series forecasting using a hybrid ARIMA and neural network model," Neurocomputing, Vol. 50, pp. 159-175, 2003.
11. Faruk, D. O., "A hybrid neural network and ARIMA model for water quality time series prediction," Engineering Applications of Artificial Intelligence, Vol. 23, No. 4, pp. 586-594, 2010.
12. Zhang, G., Patuwo, B. E., and Hu, M. Y., "Forecasting with artificial neural networks: The state of the art," International Journal of Forecasting, Vol. 14, No. 1, pp. 35-62, 1998.
13. Pan, Y.-R., Shih, Y.-T., Horng, R.-H., and Lee, A.-C., "Advanced Parameter Identification for a Linear-Motor-Driven Motion System Using Disturbance Observer," Int. J. Precis. Eng. Manuf., Vol. 10, No. 4, pp. 35-47, 2009.
14. Wolkenhauer, O., "Data Engineering: Fuzzy Mathematics in Systems Theory and Data Analysis," John Wiley & Sons, 2001.
15. Taskaya-Temizel, T. and Casey, M. C., "A comparative study of autoregressive neural network hybrids," Neural Networks, Vol. 18, No. 5-6, pp. 781-789, 2005.
16. Wu, C. L., Cahu, K. W., and Fan, C., "Prediction of rainfall time series using modular artificial neural networks coupled with data-preprocessing techniques," Journal of Hydrology, Vol. 389, No. 1-2, pp. 146-167, 2010.
17. Carcenac, M., "A modular neural network for super-resolution of human faces," Applied Intelligence, Vol. 30, No. 2, pp. 168-186, 2009.
18. Mendivil, S. G., Castillo, O., and Melin, P., "Optimization of artificial neural network architectures for time series prediction using parallel genetic algorithms," Soft Computing for Hybrid Intelligent Systems, Vol. 154, pp. 387-399, 2008.
19. Kourakos, G. and Mantoglou, A., "Pumping optimization of coastal aquifers based on evolutionary algorithms and surrogate modular neural network models," Advances in Water Resources, Vol. 32, No. 4, pp. 507-521, 2009.
20. Wang, P., Xu, L., Zhou, S.-M., Fan, Z., Li, Y., and Feng, S., "A novel Bayesian learning method for information aggregation in modular neural networks," Expert Systems with Applications, Vol. 37, No. 2, pp. 1071-1074, 2010.
21. Tseng, H. C. and Almogahed, B., "Modular neural networks with applications to pattern profiling problems," Neurocomputing, Vol. 72, No. 10-12, pp. 2093-2100, 2009.
22. Zhao, Z.-Q., "A novel modular neural network for imbalanced classification problems," Pattern Recognition Letters, Vol. 30, No. 9, pp. 783-788, 2009.
23. Mendoza, O., Melin, P., and Licea, G., "A hybrid approach for image recognition combining type-2 fuzzy logic, modular neural networks and the Sugeno integral," Information Sciences, Vol. 179, No. 13, pp. 2078-2101, 2009.
24. Ahmad, U., Gavrilov, A. V., Lee, S., and Lee, Y.-K., "A modular classification model for received signal strength based location systems," Neurocomputing, Vol. 71, No. 13, pp. 2657-2669, 2008.
25. Ramirez-Agundis, A., Gadea-Girones, R., and Colom-Palero, R., "A hardware design of a massive-parallel, modular NN-based vector quantizer for real-time video coding," Microprocessors and Microsystems, Vol. 32, No. 1, pp. 33-44, 2008.
26. Bhende, C. N., Mishra, S., and Panigrahi, B. K., "Detection and classification of power quality disturbances using S-transform and modular neural network," Electric Power Systems Research, Vol. 78, No. 1, pp. 122-128, 2008.
27. Macleod, C., Maxwell, G., and Muthuraman, S., "Incremental growth in modular neural networks," Engineering Applications of Artificial Intelligence, Vol. 22, No. 4-5, pp. 660-666, 2009.
28. Schmidt, A., "A modular neural network architecture with additional generalization abilities for high dimensional input vectors," M.Sc. Thesis, Department of Computing, Manchester Metropolitan University, 1996.
29. Relvas, C., Ramos, A., Completo, A., and Simoes, J. A., "Accuracy Control of Complex Surfaces in Reverse Engineering," Int. J. Precis. Eng. Manuf., Vol. 12, No. 6, pp. 1035-1042, 2011.
30. Kwak, Y.-K., Kim, S.-H., and Ahn, J.-H., "Improvement of Positioning Accuracy of Magnetostrictive Actuator by Means of Built-in Air Cooling and Temperature Control," Int. J. Precis. Eng. Manuf., Vol. 12, No. 5, pp. 829-834, 2011.